Scenario-Based Stochastic MPC for Energy Hubs with EV Fleets Under Persistent Grid Outages
For microgrid operators, this work demonstrates that explicitly anticipating grid outages is essential for economic and sustainability benefits, addressing a gap in integrating probabilistic outages with EV charging.
This paper develops a scenario-based stochastic MPC for microgrid energy hubs with EV fleets under probabilistic grid outages. Using real data from Ghana, the SMPC achieves performance within 1% of a perfect-forecast benchmark, while naive MPC assuming continuous grid availability offers no advantage over rule-based control.
Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper addresses this gap by developing a scenario-based stochastic model predictive controller (SMPC) for a microgrid energy hub comprising solar generation, battery storage, diesel backup, and an EV fleet connected to a weak grid. Grid outage and campus load scenarios are generated from a continuous-time Markov chain and a Gaussian Process, respectively. Using 2023 operational data from the Ashesi University Energy Hub in Ghana, we demonstrate that the SMPC achieves performance within 1\% of a perfect-forecast benchmark. In contrast, a naive MPC that assumes continuous grid availability offers no economic or sustainability advantage over rule-based control, with both incurring significantly higher costs and emissions than the SMPC. These results highlight that outage anticipation is essential for economic viability. Finally, we show that incorporating a deterministic buffer against EV consumption uncertainty eliminates over 90\% of state-of-charge violations with negligible impact on total operating costs